where \(a\)  and \(b\)  are labels for fake and real data, while \(c\)  denotes a value that \({G}\)  tricks \({D}\)  to believe for fake data [1]}.
Introduced in [2]} for further stabilisation of training and high quality image generation, we also consider the Wasserstein GAN with gradient penalty (WGAN-GP). It considers real data, simulated data and a combination of these in its objective function, which in the conditional setting has the following form [2]}:
\(\begin{split}\min _{{G}} \max _{{D}} V({D},{G}) = & \mathbb {E}_{x}[ {D}(x,{G}(x))] - \\\mathbb {E}_{x,{\widehat{z}_{y}}}[{D}(x,{\widehat{z}_{y}})] & + \lambda \mathbb {E}_{\hat{z}}[(||\nabla _{\hat{z}}{D}(\hat{z})||_2-1)^2] \,\end{split}\) 
